code: def _groups_reduce(
      group_stats: List[
          Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
      ]
  ) -> Dict[str, torch.Tensor]:
      """Compute rates for all the group statistics."""
      return {
          f"group_{group}": torch.stack(stats)
          / torch.stack(stats).sum()
          for group, stats in enumerate(group_stats)
      }
  
file_path: torchmetrics/src/torchmetrics/functional/classification/group_fairness.py
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from typing_extensions import Literal
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.compute import _safe_divide
  from torchmetrics.utilities.data import _flexible_bincount
label_desc: Use image node_id 4 for calling a global function with example usage: _groups_reduce(group_stats) and returns: unknown